Materialize vs RisingWave: Comprehensive Comparison and Analysis

From data-intensive UIs to AI agents and beyond, access to fresh data is becoming increasingly important in every industry. The problem is that joining and transforming live data often means using complex stream processors like Flink, which need specialist engineers to implement and maintain. Both RisingWave and Materialize aim to simplify working with live data, but they make fundamentally different guarantees once systems are exposed to real production conditions—large transactions, mutable data, and late or out-of-order updates—where correctness failures become visible, not theoretical. This comparison focuses not just on features, but on which system can be trusted to return correct results when those conditions inevitably occur.

When teams need live data, they typically start by querying their operational databases directly. This can work for a time, but often results in scaling and performance issues as queries get more complex or when multiple databases are involved. To address this, many teams turn to custom solutions built on stream processors such as Apache Flink.

A data stream processor ingests data from an intermediary message service, such as Kafka, and then transforms and shapes data for specific use cases. Data and software engineers can then access it via a serving layer like an API, or through a familiar data query language like SQL.

Traditionally, such an architecture has been difficult for most companies to implement and utilize. There are a couple of reasons for this:

• Many moving pieces: This architecture requires creating or standing up multiple robust and scalable components, such as message brokers, data transformation engines, and in-memory databases.
• Distributed system challenges: There are also serious problems around data consistency and performance that can only be solved by dedicated experts. This creates a high barrier to entry for companies with smaller tech teams that need to run real-time use cases.
• Ingestion complexity: Some implementations require accessing data through a custom API as opposed to using more accessible data serving solutions, such as SQL.

Enter solutions like RisingWave and Materialize, which simplify working with live data by providing a data processing and serving layer that solves these thorny issues out of the box. Both platforms abstract away the mechanics of streaming and transforming data, and they both excel at moving computation work to the write side by handling queries when data changes.

They do this via incrementally materialized views, which automatically update the results of specific queries as new data comes in upstream — changing only the affected data, without requiring expensive full recomputation.

RisingWave and Materialize are both data platforms designed to simplify live data processing.

• RisingWave is a streaming database optimized for high-throughput analytics over append-only event streams. Its architecture prioritizes ingestion efficiency and continuous aggregation, but introduces bounded correctness guarantees when applied to mutable operational data, meaning queries can observe partial states or joins that reflect processing-time rather than event-time under normal CDC conditions.
• Materialize is the live data layer for apps and AI agents that maintains the results of SQL queries over continuously changing upstream data. It is optimized for dynamic stateful workloads, offering strong consistency guarantees and full support for inserts, updates, and deletes, and helping AI agents stay in sync with reality.

• Distributed engines: Both Materialize and RisingWave are natively distributed platforms.
• High-throughput ingestion: Both can process millions of events per second with efficient resource utilization.
• Incremental view maintenance: Both update query results as upstream data changes, maintaining current views without expensive recomputation.

RisingWave is well-suited for streaming analytics workloads where data is primarily append-only, transactions are small, and eventual consistency is acceptable. It performs well for continuous aggregation, time-series analysis, and event-driven analytics, but its architecture imposes hard limits when used against mutable operational data replicated via CDC, including non-atomic visibility of large transactions.

• Append-only optimization: Designed primarily for append-only or append-mostly streams typical of analytics workloads
• Bounded transactional guarantees: Large CDC transactions are applied incrementally, exposing partial states during processing
• No global logical time for mutable data: Time-based semantics are restricted to append-only tables, forcing processing-time evaluation for CDC

Materialize’s primary focus is unifying and transforming operational data into live, composable data products. This makes it ideal for context engineering, event-driven architectures, and data-intensive UIs.

Materialize delivers critical enterprise capabilities:

• Strong consistency: Guarantees strict-serializable consistency across all data sources and queries, essential for operational workloads
• Complex live queries: Supports multi-way joins, aggregations, window functions, recursive CTEs, and sophisticated SQL operations on live data
• Millisecond latency: Single-millisecond response times for querying pre-computed results
• Enterprise data integration: Robust connectors and Change Data Capture (CDC) support for seamless integration with existing data infrastructure
• AI-ready architecture: Purpose-built to support agentic AI applications requiring live, semantically rich business context

Both platforms aim to democratize live data processing. Despite the architectural similarities, there are differences in the way each system is designed, built, and tuned — differences that ultimately make a real difference in consistency, performance, and workload suitability.

Both platforms support materialized views as their core abstraction, but with differences in capabilities and guarantees.

Materialize provides strong consistency guarantees, enforcing strict-serializable consistency across all sources and queries. Materialize's strong consistency is essential because, under Materialize's performance model, new inputs are applied incrementally without full recomputation. Even though there may be many moving parts when a query comes in, Materialize ensures that results are strictly correct.

• Queries reflect a consistent snapshot of all input data at a specific logical timestamp.
• Updates and deletes are handled incrementally and applied in the correct transactional order.
• Multi-stream joins produce correct results without requiring users to manage timing or coordination.

Strong consistency in Materialize also means that views are composable. For example, you could combine a Customer view and an Order view into a CustomerOrder view that's guaranteed to be correct. Under the hood, Materialize uses the concept of virtual time to update the whole system every update, rather than having updates cascade through over time.

It also makes Materialize an ideal data layer for AI agents and MCPs — one they can access without taxing production systems, and with consistency guarantees all the way across. This emerging digital twin architecture makes it possible to have an exact, always-current model of your relevant business entities and their relationships, expressed in the language of your company (customers, orders, suppliers, routes) rather than low-level tables, that AI agents can query against. Materialize’s strong consistency helps agentic AI applications stay in sync with reality by immediately reflecting any changes that happen as a consequence of an action taken by the agent.

• All materialized views maintain strict-serializable consistency across concurrent updates. Once defined, Materialize maintains views incrementally (via change data capture) with strong consistency guarantees. Results update in response to data changes while preserving transactional correctness across all dependent views.
• Materialize’s core stream processing library is Timely Dataflow, originally developed by Materialize co-founder Frank McSherry at Microsoft Research more than a decade ago. Materialize utilizes Timely Dataflow to automatically manage consistency across complex view hierarchies, without requiring developers to grasp concepts like watermarks or out of order concerns.

RisingWave’s materialized view support is primarily designed for analytical workloads rather than operational systems. RisingWave provides snapshot consistency over its internal state, but does not apply all source updates at a single logical time when ingesting mutable CDC data. Large transactions can be split and applied incrementally, allowing queries to observe partial states that never existed in the source database. In addition, because event-time semantics are limited to append-only tables, joins over CDC data default to processing time, which can enrich late-arriving facts with future state.

The practical consequence is that query results can reflect states that never existed in the source database, or states that don’t correspond to any valid snapshot in time

The upshot: Materialize’s strong consistency makes it suitable for mission-critical operational workloads where correctness is non-negotiable, including financial systems, regulatory reporting, and AI agent architectures requiring accurate real-time business context.

Materialize ingests CDC directly and maintains primary-key correctness, including retractions for updates and deletes. RisingWave requires users to build and maintain explicit deduplication/retraction logic to preserve ‘one current row per key’ semantics when modeling mutable CDC data; without it, joins and aggregates can overcount.

• Materialize implements an "always-ready" performance model optimized for operational workloads. Query results are continuously maintained and immediately available, with single-digit millisecondresponse times for materialized views regardless of data volume. Materialize’s performance depends on data change volume, not total data size, and it is designed to handle LLM-scale workloads — massive query volumes from AI agents and automated systems
• RisingWave implements a performance model focused on high-throughput ingestion, optimized for processing millions of events per second. RisingWave is designed for resource-efficient continuous processing, even when that requires relaxing atomicity or temporal guarantees.

RisingWave was one of the first simplified streaming solutions to market. With both open-source and cloud solutions available, it’s easy to get started quickly for streaming analytics workloads.

Materialize has worked for years with some of the leading researchers in the industry to build a platform that delivers strongly consistent materialized views with sub-millisecond performance. And the company is committed to pushing those gains even further. For example, Materialize's recent release delivers a 13% boost in speed while using 7% less memory.

Materialize provides enterprise-grade production and operational capabilities, including advanced monitoring (with comprehensive observability tools and freshness dashboards) and enterprise security features like role-based access control.

RisingWave and Materialize both address the need for accessible live data processing, but they serve somewhat different purposes in modern data architectures.

RisingWave’s use cases assume append-only inputs or workloads where enrichment does not require strict transactional or event-time correctness:

• Streaming analytics. Store and query highly volatile data, such as stock prices or venue capacity, to make real-time decisions (e.g., selling or trading stock).
• Real-time enrichment over append-only behavioral events. Performing personalization on the fly based on a user's actions on a website - e.g., serving custom discounts based on their browsing behavior.
• Feature engineering. Creating vector embeddings from data for machine learning and AI models, enabling real-time inference for situations such as making real-time bids for ads.

These use cases assume that transient inconsistencies or processing-time semantics do not violate business invariants.

Materialize covers all of these use cases, as well as:

• Digital twins for AI agents. AI agents are AI-powered processes that process data and make decisions autonomously, absent human input. By definition, these agents can’t rely on ETL processes that run every six hours — their function utterly depends on the current state of reality. Using Materialize, you can create a digital twin that provides agents with tight feedback loops, enabling them to complete tasks faster and more reliably.
• OLTP and data warehouse offload. Many companies are running real-time workloads on transactional or analytical databases that weren’t built to run them. This drags down the performance of critical operations, such as e-commerce and reporting. Thanks to its PostgreSQL wire compatibility, it’s easy to move these workloads downstream to Materialize.
• Manufacturing. Analyze and detect manufacturing floor anomalies and deliver real-time notifications so that operators can fix problems before they cause an expensive work stoppage.
• Logistics. Enable delivery tracking and notification to customers with data teams as small as a single person and at a fraction of the cost of a custom streaming solution.

Consider RisingWave when your workload consists primarily of append-only event streams, transactions are small, and downstream consumers can tolerate eventual consistency and are not sensitive to transient incorrect results during processing. RisingWave is a good fit for exploratory analytics, dashboards, and continuous aggregation where throughput and cost efficiency matter more than strict correctness guarantees.

Materialize is purpose-built for building live data products across operational sources, where consistency, reliability, and sophisticated query capabilities are paramount. It excels at efficiently maintaining always-current views of your data while providing enterprise-grade operational characteristics. Use cases include:

• Mission-critical applications: Systems requiring strong consistency for operational workloads
• AI agent architectures: Digital twins and real-time context for autonomous systems
• Live materialized views: Sophisticated SQL operations over changing data
• Composable data products: Live data products for powering AI agents, data-intensive UIs and event-driven architectures
• Database offloading: Moving real-time workloads from OLTP systems

• Strong consistency guarantees for operational workloads
• Complex SQL operations over changing data
• Enterprise-grade reliability and operational maturity
• Live data products for customer-facing applications
• Digital twin architectures for AI agents and autonomous systems

• Materialize is source-available under the BSL license with a free Community Edition. Enterprise features require commercial licensing or Materialize Cloud.
• RisingWave is fully open source under the Apache 2.0 license with self-hosted and cloud deployment options.

Both platforms offer mature SQL interfaces, growing ecosystem integrations, and active development communities, but Materialize provides more comprehensive enterprise support and operational tooling.